{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hidden Markov Model in Pytorch\n",
    "## Package Includes (so far):\n",
    "* **Viterbi Algorithm**\n",
    "    * Maximum a Posteri\n",
    "<br>\n",
    "<br>\n",
    "* **Forward-Backward Algorithm**\n",
    "    * Posterior Marginals\n",
    "<br>\n",
    "<br>\n",
    "* **Baum Welch Algorithm**\n",
    "    * Expectation Maximization Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HMM Fundamentals\n",
    "\n",
    "**HMM recap:**\n",
    "* Undirected graphical model.\n",
    "* Connections between nodes indicate dependence.\n",
    "* We observe $Y_1$ through $Y_n$, which we model as being observed from hidden states $S_1$ through $S_n$.\n",
    "* Any particular state variable $S_k$ depends only on $S_{k−1}$ (what came before it), $S_{k+1}$ (what comes after it), and $Y_k$ (the observation associated with it).\n",
    "\n",
    "** HMM are specified by three sets of parameters**\n",
    "\n",
    "* ** Transition distribution:**\n",
    "    * describes the distribution for the next state given the current state.\n",
    "    * $P(next State| current State)$\n",
    "<br>\n",
    "<br>\n",
    "* ** Emission distribution:**\n",
    "    * describes the distribution for the output given the current state.\n",
    "    * $P(Observation|current State)$\n",
    "<br>\n",
    "<br>\n",
    "* ** Initial state distribution:**\n",
    "    * describes the starting distribution over states.\n",
    "    * $P(initial State)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/hmm.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State Transitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/trans2.png\" width=\"500\" height=\"500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viterbi Algorithm\n",
    "* Efficient way of finding the most likely state sequence.\n",
    "* Method is general statistical framework of compound decision theory. \n",
    "* Maximizes a posteriori probability recursively.\n",
    "* Assumed to have a finite-state discrete-time Markov process.\n",
    "\n",
    "**Maximum a posteri (MAP) probability, given by:** <br>\n",
    "$$P(States|Observations) = \\dfrac{P(Observations)| States)P(States)}{P(Observations)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a hidden Markov model (HMM) with:\n",
    "* State space S\n",
    "* Initial probabilities $\\pi_i$ of being in state $i$\n",
    "* Transition probabilities $a_{i,j}$ of transitioning from state i to state j.\n",
    "* We observe outputs $y_1$,$\\dots$, $y_T$.\n",
    "* The most likely state sequence $x_1$,$\\dots$,$x_T$ that produces the observations is given by the recurrence relations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ V_{1,k} = P(y_1 | k) \\cdot \\pi_k$$\n",
    "\n",
    "$$ V_{t,k} = max_x(P(y_t|k)\\cdot a_{x,k} \\cdot V_{t-1,x}$$\n",
    "\n",
    "* $V_{t,k}$ is the probability of the most probable state sequence $\\mathrm{P}\\big(x_1,\\dots,x_T,y_1,\\dots, y_T\\big)$\n",
    "* The Viterbi path can be retrieved by saving back pointers that remember which state x was used in the second equation.\n",
    "* Let $\\mathrm{Ptr}(k,t)$ be the function that returns the value of x used to compute $V_{t,k}$ if $t > 1$, or $k$ if $t=1$. Then:\n",
    "\n",
    "$$ x_T = argmax_x(V_{T,x})$$\n",
    "\n",
    "$$ x_{t-1} = Ptr(x_{t}, t)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shortest Route Task\n",
    "The MAP sequence estimation problem previously stated can also be viewed as the problem of finding the shortest route through a certain graph.\n",
    "\n",
    "**Representing an HMM as a trellis**\n",
    "* Each node corresponds to a distinct state at a given time\n",
    "* Each arrow represents a transition to some new state at the next instant of time.\n",
    "* The trellis begins and ends at the known states c0 and cn.\n",
    "* Its most important property is that to every possible state sequence C there corresponds a unique path through the trellis, and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/graph1.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Assign to every path a length proportional to $-log [p(Observations)| States)+P(States)]$.\n",
    "* $log()$ is a monotonic function and there is a one-to-one correspondence between paths and sequences, we only need to find the path whose $-log [p(Observations)| States)+P(States)]$ is minimum.\n",
    "* This will give us the state sequence for which $p(Observations)| States) \\cdot P(States)$ is maximum.\n",
    "* This is the state sequence with the maximum a posteriori (MAP) probability.\n",
    "\n",
    "#### Graph Search\n",
    "Four-state trellis covering five time units."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/graph2.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recursive Steps\n",
    "The 5 recursive steps by which the algorithm determines the shortest path from the initial to the final node are shown in here. At each step only the 4 (or fewer) survivors are shown, along with their lengths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/graph3.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dptable(state_prob):\n",
    "    print(\" \".join((\"%8d\" % i) for i in range(state_prob.shape[0])))\n",
    "    for i, prob in enumerate(state_prob.T):\n",
    "        print(\"%.7s: \" % states[i] +\" \".join(\"%.7s\" % (\"%f\" % p) for p in prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highlight_max(s):\n",
    "    '''\n",
    "    highlight the maximum in a Series yellow.\n",
    "    '''\n",
    "    is_max = s == s.max()\n",
    "    return ['background-color: yellow' if v else '' for v in is_max]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from HiddenMarkovModel import HiddenMarkovModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viterbi Example\n",
    "\n",
    "Let's consider the following simple HMM.\n",
    "* Composed of 2 hidden states: Healthy and Fever.\n",
    "* Composed of 3 possible observation: Normal, Cold, Dizzy\n",
    "\n",
    "The model can then be used to predict if a person is feverish at every timestep from a given observation sequence. There are several paths through the hidden states (Healthy and Fever) that lead to the given sequence, but they do not have the same probability.\n",
    "\n",
    "The Viterbi algorithm is a dynamical programming algorithm that allows us to compute the most probable path. This package will compute recursively the probability of the most probable path. \n",
    "\n",
    "Note: for the calculations, it is convenient to use the log of the probabilities. Indeed, this allows us to compute sums instead of products, which is more efficient and accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/Viterbi.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "p0 = np.array([0.6, 0.4])\n",
    "\n",
    "emi = np.array([[0.5, 0.1],\n",
    "                [0.4, 0.3],\n",
    "                [0.1, 0.6]])\n",
    "\n",
    "trans = np.array([[0.7, 0.3],\n",
    "                  [0.4, 0.6]])\n",
    "\n",
    "states = {0:'Healthy', 1:'Fever'}\n",
    "obs = {0:'normal', 1:'cold', 2:'dizzy'}\n",
    "\n",
    "obs_seq = np.array([0, 0, 1, 2, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_p0 = pd.DataFrame(p0, index=[\"Healthy\", \"Fever\"], columns=[\"Prob\"])\n",
    "df_emi = pd.DataFrame(emi, index=[\"Normal\", \"Cold\", \"Dizzy\"], columns=[\"Healthy\", \"Fever\"])\n",
    "df_trans = pd.DataFrame(trans, index=[\"fromHealthy\", \"fromFever\"], columns=[\"toHealthy\", \"toFever\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inital state probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Healthy</th>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fever</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         Prob\n",
       "Healthy   0.6\n",
       "Fever     0.4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transition Probability Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toHealthy</th>\n",
       "      <th>toFever</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fromHealthy</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fromFever</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             toHealthy  toFever\n",
       "fromHealthy        0.7      0.3\n",
       "fromFever          0.4      0.6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emission Probability Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Healthy</th>\n",
       "      <th>Fever</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cold</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dizzy</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Healthy  Fever\n",
       "Normal      0.5    0.1\n",
       "Cold        0.4    0.3\n",
       "Dizzy       0.1    0.6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Viterbi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation sequence:  ['normal', 'normal', 'cold', 'dizzy', 'dizzy']\n"
     ]
    },
    {
     "data": {
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       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >0</th> \n",
       "        <th class=\"col_heading level0 col1\" >1</th> \n",
       "        <th class=\"col_heading level0 col2\" >2</th> \n",
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       "<tbody>    <tr> \n",
       "        <th id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05alevel0_row0\" class=\"row_heading level0 row0\" >Healthy</th> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow0_col0\" class=\"data row0 col0\" >0.3</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow0_col1\" class=\"data row0 col1\" >0.105</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow0_col2\" class=\"data row0 col2\" >0.0294</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow0_col3\" class=\"data row0 col3\" >0.002058</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow0_col4\" class=\"data row0 col4\" >0.00021168</td> \n",
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       "        <th id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05alevel0_row1\" class=\"row_heading level0 row1\" >Fever</th> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow1_col0\" class=\"data row1 col0\" >0.04</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow1_col1\" class=\"data row1 col1\" >0.009</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow1_col2\" class=\"data row1 col2\" >0.00945</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow1_col3\" class=\"data row1 col3\" >0.005292</td> \n",
       "        <td id=\"T_7fa3374c_3348_11e9_ac9d_38baf892a05arow1_col4\" class=\"data row1 col4\" >0.00190512</td> \n",
       "    </tr></tbody> \n",
       "</table> "
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       "<pandas.io.formats.style.Styler at 0x2168469ee80>"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model =  HiddenMarkovModel(trans, emi, p0)\n",
    "\n",
    "states_seq, state_prob = model.viterbi_inference(obs_seq)\n",
    "\n",
    "print(\"Observation sequence: \", [obs[o] for o in obs_seq])\n",
    "df = pd.DataFrame(torch.t(state_prob).cpu().numpy(), index=[\"Healthy\", \"Fever\"])\n",
    "df.style.apply(highlight_max, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most likely States:  ['Healthy', 'Healthy', 'Healthy', 'Fever', 'Fever']\n"
     ]
    }
   ],
   "source": [
    "print(\"Most likely States: \",[states[s.item()] for s in states_seq])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward - Backward Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Overview **\n",
    "* The goal of the forward-backward algorithm is to find the conditional distribution over hidden states given the data.\n",
    "\n",
    "* It is used to find the most likely state for any point in time.\n",
    "* It cannot, however, be used to find the most likely sequence of states (see Viterbi)\n",
    "\n",
    "**The forward–backward algorithm:**\n",
    "* Inference algorithm for hidden Markov models.\n",
    "* Computes posterior marginals of all hidden state variables given a sequence of observations/emissions.\n",
    "* Computes, for all hidden state variables $S_k \\in \\{S_1, \\dots, S_t\\}$, the distribution $P(S_k\\ |\\ o_{1:t})$.\n",
    "* This inference task is usually called smoothing.\n",
    "* The algorithm makes use of the principle of dynamic programming to compute efficiently the values that are required to obtain the posterior marginal distributions in two passes.\n",
    "* The first pass goes forward in time while the second goes backward in time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of the Forward and Backward Messages\n",
    "* Each message is a table that indicates what the node at the start point believes about the node at the end point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/FB1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing Message Passing\n",
    "**Forward message**\n",
    "* $α_k$ represents a message from $k − 1$ to $k$ that includes $p_{Y|X}(y_k|x_k)$.\n",
    "<br>\n",
    "\n",
    "** Backward message** \n",
    "* $β_k$ represents a message from $k + 1$ to $k$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/FB3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability interpretation of message passing\n",
    "\n",
    "**Forward message**\n",
    "* The probability of ending up in any particular state given the first k observations in the sequence.\n",
    "<br>\n",
    "\n",
    "\n",
    "** Backward message** \n",
    "* The probability of observing the remaining observations given any starting point k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/eq3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoothing Step\n",
    "** Represent the conditional probability of being in state $S_i$ at time $t$ given the observation sequence**\n",
    "\n",
    "* The initial forward $α$ message is initialized to $α_1(x_1) = P_{X_1}(x_1)P_{Y|X}(y_1|x_1)$.\n",
    "* To obtain a marginal distribution, we simply multiply the messages together and normalize:\n",
    "* The smoothing step allows the algorithm to take into account any past observations of output for computing more accurate results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/eq4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Message Passing for  $m_{2→3}(x_3)$\n",
    "In order for node 2 to summarize its belief about $X_3$, it must incorporate: \n",
    "* The previous message $m_{1→2}(x_2)$\n",
    "* Its observation $p_{Y|X}(y_2|x_2)$\n",
    "* The relationship $W(x_3|x_2)$ between $X_2$ and $X_3$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/FB4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forward-Backward Example\n",
    "\n",
    "Let's condiser a situation where you work in a shopping mall with no views to the outside.\n",
    "You wish to infer the weather outside (Rain, No Rain) at the present moment, given only obervations of passerby with or without an umbrella."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "p0 = np.array([0.5, 0.5])\n",
    "\n",
    "emi = np.array([[0.9, 0.2],\n",
    "                [0.1, 0.8]])\n",
    "\n",
    "trans = np.array([[0.7, 0.3],\n",
    "                  [0.3, 0.7]])\n",
    "\n",
    "states = {0:'rain', 1:'no_rain'}\n",
    "obs = {0:'umbrella', 1:'no_umbrella'}\n",
    "\n",
    "obs_seq = np.array([1, 1, 0, 0, 0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Run Forward-Backward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model =  HiddenMarkovModel(trans, emi, p0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.N = len(obs_seq)\n",
    "\n",
    "shape = [model.N, model.S]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.initialize_forw_back_variables(shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_prob_seq = model.E[obs_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.forward_backward(obs_prob_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.forward * model.backward\n",
    "\n",
    "# marginal per timestep\n",
    "marginal = torch.sum(posterior, 1)\n",
    "        \n",
    "# Normalize porsterior into probabilities\n",
    "posterior = posterior / marginal.view(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Forward\n",
      "       0        1        2        3        4        5\n",
      "rain: 0.11111 0.06163 0.68386 0.85819 0.89029 0.19256\n",
      "no_rain: 0.88888 0.93837 0.31613 0.14180 0.10971 0.80743\n",
      "\n",
      "\n",
      "Backward\n",
      "       0        1        2        3        4        5\n",
      "rain: 1.40699 3.23616 2.72775 1.73108 1.39911 2.93497\n",
      "no_rain: 2.32412 1.69423 1.50282 1.24784 2.66283 2.93497\n",
      "\n",
      "\n",
      "Posterior\n",
      "       0        1        2        3        4        5\n",
      "rain: 0.07035 0.11146 0.79701 0.89357 0.81002 0.19256\n",
      "no_rain: 0.92965 0.88853 0.20298 0.10643 0.18997 0.80743\n",
      "\n",
      "============================================================\n",
      "Most likely Final State:  no_rain\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "results = [model.forward.cpu().numpy(), model.backward.cpu().numpy(), posterior.cpu().numpy()]\n",
    "result_list = [\"Forward\", \"Backward\", \"Posterior\"]\n",
    "\n",
    "for state_prob, path in zip(results, result_list) :\n",
    "    inferred_states = np.argmax(state_prob, axis=1)\n",
    "    print()\n",
    "    print(path)\n",
    "    dptable(state_prob)\n",
    "    print()\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"Most likely Final State: \",states[inferred_states[-1]])\n",
    "print(\"=\"*60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baum Welch Algorithm\n",
    "\n",
    "Baum–Welch algorithm is used to infer unknown parameters of a Hidden Markov Model.\n",
    "\n",
    "**Model Parameters:**\n",
    "* Initial State Probabilities\n",
    "* Transition Matrix\n",
    "* Emission Matrix\n",
    "\n",
    "\n",
    "** Expectation-Maximization ** <br>\n",
    "It makes use of the forward-backward algorithm to update the hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/EM3.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Expectation-Maximization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/EM1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Variable Updates (Expectation step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate the temporary variables, according to Bayes' theorem:** <br>\n",
    "It's the probability of being in state $i$ at time $t$ given the observed sequence $Y$ and the parameters $\\theta$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\gamma_i(t)=P(X_t=i|Y,\\theta) = \\frac{\\alpha_i(t)\\beta_i(t)}{\\sum_{j=1}^N \\alpha_j(t)\\beta_j(t)}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of being in state $i$ and $j$ at times $t$ and $t+1$ respectively given the observed sequence $Y$ and parameters $\\theta$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\xi_{ij}(t)=P(X_t=i,X_{t+1}=j|Y,\\theta)=\\frac{\\alpha_i(t) a_{ij} \\beta_j(t+1) b_j(y_{t+1})}{\\sum_{k=1}^N \\alpha_k(T)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Update Inital State Probability**<br>\n",
    "It's expected frequency spent in state i at time 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\pi_i^* = \\gamma_i(1)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Update Transition Matrix** <br>\n",
    "It's the expected number of transitions from state $i$ to state $j$ compared to the expected total number of transitions away from state $i$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ a_{ij}^*=\\frac{\\sum^{T-1}_{t=1}\\xi_{ij}(t)}{\\sum^{T-1}_{t=1}\\gamma_i(t)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Update Emission Matrix** <br>\n",
    "It's the expected number of times the output observations have been equal to $v_k$ while in state $i$ over the expected total number of times in state $i$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$b_i^*(v_k)=\\frac{\\sum^T_{t=1} 1_{y_t=v_k} \\gamma_i(t)}{\\sum^T_{t=1} \\gamma_i(t)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "* It is possible to over-fit a particular data set. That is $P(Y|\\theta_{final})>P(Y|\\theta_{true})$.\n",
    "* The algorithm also does not guarantee a global maximum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Baum-Welch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Generator Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_HMM_observation(num_obs, pi, T, E):\n",
    "    def drawFrom(probs):\n",
    "        return np.where(np.random.multinomial(1,probs) == 1)[0][0]\n",
    "\n",
    "    obs = np.zeros(num_obs)\n",
    "    states = np.zeros(num_obs)\n",
    "    states[0] = drawFrom(pi)\n",
    "    obs[0] = drawFrom(E[:, int(states[0])])\n",
    "    for t in range(1,num_obs):\n",
    "        states[t] = drawFrom(T[int(states[t-1]),:])\n",
    "        obs[t] = drawFrom(E[:, int(states[t])])\n",
    "    return np.int64(obs), states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### True Parameters that Generated the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "True_pi = np.array([0.5, 0.5])\n",
    "\n",
    "True_T = np.array([[0.85, 0.15],\n",
    "                  [0.12, 0.88]])\n",
    "\n",
    "True_E = np.array([[0.8, 0.0],\n",
    "                   [0.1, 0.0],\n",
    "                   [0.1, 1.0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a Sample of 50 Observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_seq, states = generate_HMM_observation(200, True_pi, True_T, True_E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Obersvations:   [2 2 2 2 2 2 2 2 2 2]\n",
      "First 10 Hidden States:  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "print(\"First 10 Obersvations:  \", obs_seq[:10])\n",
    "print(\"First 10 Hidden States: \", states[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obs_seq = np.array([2, 2, 2, 2, 0, 1, 1, 2, 0, 0], dtype=np.int64)\n",
    "# states = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize to Arbitrary Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_pi = np.array([0.5, 0.5])\n",
    "\n",
    "init_T = np.array([[0.5, 0.5],\n",
    "                  [0.5, 0.5]])\n",
    "\n",
    "init_E = np.array([[0.3, 0.2],\n",
    "                   [0.3, 0.5],\n",
    "                   [0.4, 0.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model =  HiddenMarkovModel(init_T, init_E, init_pi, epsilon=0.0001, maxStep=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "converged at step 72\n",
      "Transition Matrix: \n",
      "tensor([[0.8833, 0.1167],\n",
      "        [0.0794, 0.9206]], dtype=torch.float64)\n",
      "\n",
      "Emission Matrix: \n",
      "tensor([[8.3184e-01, 2.2193e-03],\n",
      "        [9.5418e-02, 6.0206e-10],\n",
      "        [7.2745e-02, 9.9778e-01]], dtype=torch.float64)\n",
      "\n",
      "Reached Convergence: \n",
      "True\n"
     ]
    }
   ],
   "source": [
    "trans0, transition, emission, converge = model.Baum_Welch_EM(obs_seq)\n",
    "\n",
    "print(\"Transition Matrix: \")\n",
    "print(transition)\n",
    "print()\n",
    "print(\"Emission Matrix: \")\n",
    "print(emission)\n",
    "print()\n",
    "print(\"Reached Convergence: \")\n",
    "print(converge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of Probability of State 1 over multiple training steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_summary = np.array([model.prob_state_1[i].cpu().numpy() for i in range(len(model.prob_state_1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(1 - state_summary[[0, 4, 6, 8, 9, 10]].T)\n",
    "plt.ylim(-0.1,1.1)\n",
    "plt.title('Probability State=1 over time')\n",
    "plt.xlabel('Time')\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of True State over Guess Probability of State=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(states.T,'-o',alpha=0.7)\n",
    "plt.plot(1 - state_summary.T, '-o',alpha=0.7)\n",
    "plt.legend(('True State','Guessed Probability of State=1'), loc = 'right')\n",
    "plt.ylim(-0.1,1.1)\n",
    "plt.xlabel('Time')\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beware of Overfitting\n",
    "\n",
    "* The algorithm is clearly learning to generate the correct hidden state.\n",
    "* Baum Welch does, however, overfit quickly.\n",
    "* It is important to:\n",
    "    * Train on multiple sequences.\n",
    "    * Regularize training.\n",
    "    * Repeat inference with multiple random initial parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.97\n"
     ]
    }
   ],
   "source": [
    "pred = (1 - state_summary[-2]) > 0.5\n",
    "print(\"Accuracy: \", np.mean(pred == states))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
